IPMI 2013 - LNCS 7917

Joint Co-segmentation and Registration of 3D Ultrasound Images

Raphael Prevost^{1, 2}, Remi Cuingnet¹, Benoit Mory¹, Jean-Michel Correas³, Laurent D. Cohen², and Roberto Ardon¹

¹Philips Research Medisys, Suresnes, France

²CEREMADE UMR 7534, Universite Paris Dauphine, Paris, France

³Adult Radiology Department, Necker Hospital, Paris, France

Abstract. Contrast-enhanced ultrasound (CEUS) allows a visualization of the vascularization and complements the anatomical information provided by conventional ultrasound (US). However, these images are inherently subject to noise and shadows, which hinders standard segmentation algorithms. In this paper, we propose to use simultaneously the different information coming from 3D US and CEUS images to address the problem of kidney segmentation. To that end, we introduce a generic framework for joint co-segmentation and registration that seeks objects having the same shape in several images. From this framework, we derive both an ellipsoid co-detection and a model-based co-segmentation algorithm. These methods rely on voxel-classification maps that we estimate using random forests in a structured way. This yields a fast and fully automated pipeline, in which an ellipsoid is first estimated to locate the kidney in both US and CEUS volumes and then deformed to segment it accurately. The proposed method outperforms state-of-the-art results (by dividing the kidney volume error by two) on a clinically representative database of 64 images.

Keywords: co-segmentation, registration, kidney, random forests, ultrasound, contrast-enhanced ultrasound

LNCS 7917, p. 268 ff.

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